For the given confusion matrix, what is the recall and precision of the model?

1. A. Recall = 0.92 Precision = 0.84
2. B. Recall = 0.84 Precision = 0.8
3. C. Recall = 0.92 Precision = 0.8
4. D. Recall = 0.8 Precision = 0.92

Correct Answer: A

A Machine Learning Specialist works for a credit card processing company and needs to predict which transactions may be fraudulent in near-real time. Specifically, the Specialist must train a model that returns the probability that a given transaction may be fraudulent
How should the Specialist frame this business problem'?

1. A. Streaming classification
2. B. Binary classification
3. C. Multi-category classification
4. D. Regression classification

Correct Answer: A

A large consumer goods manufacturer has the following products on sale
• 34 different toothpaste variants
• 48 different toothbrush variants
• 43 different mouthwash variants
The entire sales history of all these products is available in Amazon S3 Currently, the company is using custom-built autoregressive integrated moving average (ARIMA) models to forecast demand for these products The company wants to predict the demand for a new product that will soon be launched
Which solution should a Machine Learning Specialist apply?

1. A. Train a custom ARIMA model to forecast demand for the new product.
2. B. Train an Amazon SageMaker DeepAR algorithm to forecast demand for the new product
3. C. Train an Amazon SageMaker k-means clustering algorithm to forecast demand for the new product.
4. D. Train a custom XGBoost model to forecast demand for the new product

Correct Answer: B
The Amazon SageMaker DeepAR forecasting algorithm is a supervised learning algorithm for forecasting scalar (one-dimensional) time series using recurrent neural networks (RNN). Classical forecasting methods, such as autoregressive integrated moving average (ARIMA) or exponential smoothing (ETS), fit a single model to each individual time series. They then use that model to extrapolate the time series into the future.

A company is running a machine learning prediction service that generates 100 TB of predictions every day A Machine Learning Specialist must generate a visualization of the daily precision-recall curve from the predictions, and forward a read-only version to the Business team.
Which solution requires the LEAST coding effort?

1. A. Run a daily Amazon EMR workflow to generate precision-recall data, and save the results in Amazon S3 Give the Business team read-only access to S3
2. B. Generate daily precision-recall data in Amazon QuickSight, and publish the results in a dashboard shared with the Business team
3. C. Run a daily Amazon EMR workflow to generate precision-recall data, and save the results in Amazon S3 Visualize the arrays in Amazon QuickSight, and publish them in a dashboard shared with the Business team
4. D. Generate daily precision-recall data in Amazon ES, and publish the results in a dashboard shared with the Business team.

Correct Answer: C

A Mobile Network Operator is building an analytics platform to analyze and optimize a company's operations using Amazon Athena and Amazon S3
The source systems send data in CSV format in real lime The Data Engineering team wants to transform the data to the Apache Parquet format before storing it on Amazon S3
Which solution takes the LEAST effort to implement?

1. A. Ingest .CSV data using Apache Kafka Streams on Amazon EC2 instances and use Kafka Connect S3 to serialize data as Parquet
2. B. Ingest .CSV data from Amazon Kinesis Data Streams and use Amazon Glue to convert data into Parquet.
3. C. Ingest .CSV data using Apache Spark Structured Streaming in an Amazon EMR cluster and use Apache Spark to convert data into Parquet.
4. D. Ingest .CSV data from Amazon Kinesis Data Streams and use Amazon Kinesis Data Firehose to convert data into Parquet.

Correct Answer: C